The goal of multiple-objective optimization, in stark contrast to the single-objective case where the global optimum is desired (except in certain multimodal cases), is to maximize or minimize multiple measures of performance simultaneously whereas maintaining a diverse set of Pareto-optimal solutions. The concept of Pareto optimality refers to the set of solutions in the feasible objective space that is non-dominated. A solution is considered to be non-dominated if it is no worse than another solution in all objectives and strictly better than that solution in at least one objective. Consider a situation where both f1 and f2 objectives are to be minimized, but where the two objectives are in conflict, at least to some extent, with each other. Because both objectives are important, there cannot be a single solution that optimizes the f1 and f2 objectives; rather, a set of optimal solutions exists which depict a tradeoff.
When the number of variables for a given optimization problem grows beyond what the current state-of-the-art methods are capable of handling, the problem is often reformulated to reduce the number of variables (through sensitivity analysis perhaps) so that existing methods can be used. For many problems, however, extensive a priori knowledge of the sensitive variables might be too costly to attain or the variable interactions are so highly coupled that reduction of the number of search variables is not possible.